Mobile-Health Tool Use and Community Health Worker Performance in the Kenyan Context: A Quasi-Experimental Post-Test Perspective

  • Maradona Gatara University of the Witwatersrand (WITS), Johannesburg

Abstract

Background and Purpose: Community Health Workers (CHW’s) are often the only link to healthcare for millions of people in the developing world. Mobile-health or ‘mHealth’ tools can support CHWs in monitoring and evaluation, disease surveillance, and point-of-care diagnostics. However, there is a lack of evidence on the impacts of mHealth on CHW performance. To address this gap, we determine a set of measures along which to evaluate the impact of mHealth tools on CHW performance.

Methods: Using a quasi-experimental post-test design we compare CHWs using an mHealth tool (n=196) with those using a paper-based system (n=199). The empirical context for the study is peri-urban communities in Kenya and data was collected using a survey instrument.

Results: Results provide evidence of impacts of mHealth tool use on objective and perceptual performance measures.

Conclusions: CHWs using mHealth tools capture and transmit higher percentages of monthly cases on time and without missing data, and are highly satisfied with the contribution of the tool to their performance.

Downloads

Download data is not yet available.

Author Biography

Maradona Gatara, University of the Witwatersrand (WITS), Johannesburg
PhD candidate at the University of the Witwatersrand (WITS), Johannesburg, South Africa (SA), Department of Information Systems (IS), School of Economic and Business Sciences (SEBS).

References

References

Mechael P, The Case for mHealth in Developing Countries. Innovations. 2009; 4(1):103-18.

One Million Community Health Workers: Technical Taskforce Report. Earth Institute [Internet]; 2010 [cited 2015 Feb 24]. Available from http://milleniumvillages.org/uploads/ReportPaper/1mCHW_Technical TaskForceReport.pdf

Liu A, Sullivan S, Khan, M, Sachs S, Singh P. Community Health Workers in Global Health. Scale and Scalability. Mt Sinai J Med. 2011; 78(1):419-35.

LeMaire J. Scaling Up Mobile Health: Elements for the Successful Scale Up of mHealth in Developing Countries. Geneva: Advanced Development for Africa [Internet]; 2011 [cited 2015 Feb 24]. Available from http://www.k4health.org/sites/default/files/ADA_mHealth%20White%20Paper.pdf

Harris A, McGregor J, Perencevich E, Furino J, Zhu J, Peterson J, et al. The Use and Interpretation of Quasi-Experimental Studies in Medical Informatics. J Am Med Inform Assoc. 2006; 13(1):16-23.

Cook T, Shadish W, Wong V. Three Conditions Under Which Experiments and Observational Studies Produce Comparable Causal Estimates: New Findings from Within-Study Comparisons. J Policy Anal Manage. 2006; 27(4):724-50.

Leedy P, Ormrod, J. Practical Research: Planning and Design. New Jersey: Pearson; 2013.

Creswell J. Research Design. Qualitative, Quantitative, and Mixed Method Approaches. California: Sage Publications; 2014.

Goodhue D, Thompson R. Task-Technology Fit and Individual Performance. MIS Quarterly. 1995; 19(2):213-36.

Goodhue, D. The Model Underlying the Measurement of the Impacts of the IIC on the End Users. J Am Soc Inf Sci. 1997; 48(5):449-53.

D’Ambra J, Wilson C. Use of the World Wide Web for International Travel: Integrating the Construct of Un-certainly in Information Seeking and the Task-Technology Fit (TTF) Model. J Am Soc Inf Sci Technol. 2004; 55(8):731-42.

Teo S, Men B. Knowledge Portals in Chinese Consulting Firms: A Task-Technology Fit Perspective. Euro-pean Journal of Information Systems. 2008; 17(1):557-74.

Cane S, McCarthy R. Analyzing the Factors that Affect Information Systems Use: A Task-Technology Fit Meta-Analysis. Journal of Computer Information Systems. 2009:108-23.

D’Ambra J, Wilson C, Akter, S. Application of the Task-Technology Fit Model to Structure and Evaluate the Adoption of E-Books by Academics. J Am Soc Inf Sci Technol. 2013; 64(1):48-64.

Belanger F, Collins R, Cheney P. Technology Requirements and Work Group Communication for Telecom-muters. Information Systems Research. 2001; 12(2):155-76.

Hou C. Examining the Effects of User Satisfaction on System Usage and Individual Performance with Business Intelligence Systems: An Empirical Study of Taiwan’s Electronics Industry. Int J Inf Manage. 2012; 32(6):560-73.

Junglas I, Abraham C, Ives B. Mobile Technology at the Frontlines of Patient Care: Understanding Fit and Human Drives in Utilization Decisions and Performance. 2009; 46(3): 534-647.

Torkzadeh G, Doll W. The Development of a Tool for Measuring the Perceived Impact of Information Tech-nology on Work. The International Journal of Management Science. 1999; 27(1):327-39.

Arango J, Iyengar M, Dunn K. Performance Factors of Mobile Rich Media Job Aids for Community Health Workers. J Am Med Inform Assoc. 2011; 18(1):131-37.

Hair J, Babin B, Money A, Samouel, P. Essentials of Business Research Methods. Chichester: Wiley; 2003.

Saunders M, Lewis P, Thornhill A. Research Methods for Business Students. Edinburgh Gate, Harlow: Pear-son; 2012.

Daniel J. Sampling Essentials: Practical Guidelines For Making Sampling Choices. California: Sage; 2012.

Vanderstoep S, Johnston D. Research Methods For Everyday Life, Blending Qualitative and Quantitative Approaches, California: Jossey-Bass, San Francisco; 2009.

Gopalan S, Mohanty S. Assessing Community Health Workers Performance Motivation: A Mixed-Methods Approach on India’s Accredited Social Health Activists (ASHA) Programme. BMJ Open. 2012; 2(5):1-11.

Ndedda C, Wamae A, Ndirangu, M, Wamalwa D, Wangalwa G, Watako P, et al. Effects of Selected Socio-Demographic Characteristics of Community Health Workers on Performance of Home Visits During Preg-nancy: A Cross-Sectional Study in Busia District, Kenya. Glob J Health Sci. 2012; 4(5):78-90.

Kruk M, Freedman L. Assessing Health System Performance in Developing Countries: A Review of the Lit-erature. Health Policy. 2008; 85(1):263-76.

Hair J, Black W, Babin B, and Anderson R. Multivariate Data Analysis. New Jersey: Prentice Hall; 2010.

Brace N, Kemp R, Snelgar R. SPSS for Psychologists. Hampshire: Palgrave Macmillan; 2012.

Ruland C. Handheld Technology to Improve Patient Care. J Am Med Inform Assoc. 2002; 9(2):192-201.

Doll W, Torkzadeh G. The Measurement of End-User Computing Satisfaction. MIS Quarterly. 1988; 12(2): 1151-71.

Igbaria M, Tan M. The Consequences of Information Technology Acceptance on Subsequent Individual Per-formance. Information and Management. 1997; 32(3):113-21.

Ives B, Olson M, Baroudi J. The Measurement of User Information Satisfaction. Commun ACM. 1983; 26(10):785-793.

DeLone W, McLean E. Information Systems Success: The Quest for the Dependent Variable. Information Systems Research. 1992; 3(1):60-95.

Published
2015-02-27
How to Cite
Gatara, M. (2015). Mobile-Health Tool Use and Community Health Worker Performance in the Kenyan Context: A Quasi-Experimental Post-Test Perspective. Journal of Health Informatics in Africa, 2(2). https://doi.org/10.12856/JHIA-2014-v2-i2-108